Agents
Detailed guide on creating and managing agents within the CrewAI framework.
Overview of an Agent
In the CrewAI framework, an Agent
is an autonomous unit that can:
- Perform specific tasks
- Make decisions based on its role and goal
- Use tools to accomplish objectives
- Communicate and collaborate with other agents
- Maintain memory of interactions
- Delegate tasks when allowed
Think of an agent as a specialized team member with specific skills, expertise, and responsibilities. For example, a Researcher
agent might excel at gathering and analyzing information, while a Writer
agent might be better at creating content.
CrewAI Enterprise includes a Visual Agent Builder that simplifies agent creation and configuration without writing code. Design your agents visually and test them in real-time.
The Visual Agent Builder enables:
- Intuitive agent configuration with form-based interfaces
- Real-time testing and validation
- Template library with pre-configured agent types
- Easy customization of agent attributes and behaviors
Agent Attributes
Attribute | Parameter | Type | Description |
---|---|---|---|
Role | role | str | Defines the agent’s function and expertise within the crew. |
Goal | goal | str | The individual objective that guides the agent’s decision-making. |
Backstory | backstory | str | Provides context and personality to the agent, enriching interactions. |
LLM (optional) | llm | Union[str, LLM, Any] | Language model that powers the agent. Defaults to the model specified in OPENAI_MODEL_NAME or “gpt-4”. |
Tools (optional) | tools | List[BaseTool] | Capabilities or functions available to the agent. Defaults to an empty list. |
Function Calling LLM (optional) | function_calling_llm | Optional[Any] | Language model for tool calling, overrides crew’s LLM if specified. |
Max Iterations (optional) | max_iter | int | Maximum iterations before the agent must provide its best answer. Default is 20. |
Max RPM (optional) | max_rpm | Optional[int] | Maximum requests per minute to avoid rate limits. |
Max Execution Time (optional) | max_execution_time | Optional[int] | Maximum time (in seconds) for task execution. |
Memory (optional) | memory | bool | Whether the agent should maintain memory of interactions. Default is True. |
Verbose (optional) | verbose | bool | Enable detailed execution logs for debugging. Default is False. |
Allow Delegation (optional) | allow_delegation | bool | Allow the agent to delegate tasks to other agents. Default is False. |
Step Callback (optional) | step_callback | Optional[Any] | Function called after each agent step, overrides crew callback. |
Cache (optional) | cache | bool | Enable caching for tool usage. Default is True. |
System Template (optional) | system_template | Optional[str] | Custom system prompt template for the agent. |
Prompt Template (optional) | prompt_template | Optional[str] | Custom prompt template for the agent. |
Response Template (optional) | response_template | Optional[str] | Custom response template for the agent. |
Allow Code Execution (optional) | allow_code_execution | Optional[bool] | Enable code execution for the agent. Default is False. |
Max Retry Limit (optional) | max_retry_limit | int | Maximum number of retries when an error occurs. Default is 2. |
Respect Context Window (optional) | respect_context_window | bool | Keep messages under context window size by summarizing. Default is True. |
Code Execution Mode (optional) | code_execution_mode | Literal["safe", "unsafe"] | Mode for code execution: ‘safe’ (using Docker) or ‘unsafe’ (direct). Default is ‘safe’. |
Multimodal (optional) | multimodal | bool | Whether the agent supports multimodal capabilities. Default is False. |
Inject Date (optional) | inject_date | bool | Whether to automatically inject the current date into tasks. Default is False. |
Date Format (optional) | date_format | str | Format string for date when inject_date is enabled. Default is “%Y-%m-%d” (ISO format). |
Reasoning (optional) | reasoning | bool | Whether the agent should reflect and create a plan before executing a task. Default is False. |
Max Reasoning Attempts (optional) | max_reasoning_attempts | Optional[int] | Maximum number of reasoning attempts before executing the task. If None, will try until ready. |
Embedder (optional) | embedder | Optional[Dict[str, Any]] | Configuration for the embedder used by the agent. |
Knowledge Sources (optional) | knowledge_sources | Optional[List[BaseKnowledgeSource]] | Knowledge sources available to the agent. |
Use System Prompt (optional) | use_system_prompt | Optional[bool] | Whether to use system prompt (for o1 model support). Default is True. |
Creating Agents
There are two ways to create agents in CrewAI: using YAML configuration (recommended) or defining them directly in code.
YAML Configuration (Recommended)
Using YAML configuration provides a cleaner, more maintainable way to define agents. We strongly recommend using this approach in your CrewAI projects.
After creating your CrewAI project as outlined in the Installation section, navigate to the src/latest_ai_development/config/agents.yaml
file and modify the template to match your requirements.
Variables in your YAML files (like {topic}
) will be replaced with values from your inputs when running the crew:
Here’s an example of how to configure agents using YAML:
To use this YAML configuration in your code, create a crew class that inherits from CrewBase
:
The names you use in your YAML files (agents.yaml
) should match the method names in your Python code.
Direct Code Definition
You can create agents directly in code by instantiating the Agent
class. Here’s a comprehensive example showing all available parameters:
Let’s break down some key parameter combinations for common use cases:
Basic Research Agent
Code Development Agent
Long-Running Analysis Agent
Custom Template Agent
Date-Aware Agent with Reasoning
Reasoning Agent
Multimodal Agent
Parameter Details
Critical Parameters
role
,goal
, andbackstory
are required and shape the agent’s behaviorllm
determines the language model used (default: OpenAI’s GPT-4)
Memory and Context
memory
: Enable to maintain conversation historyrespect_context_window
: Prevents token limit issuesknowledge_sources
: Add domain-specific knowledge bases
Execution Control
max_iter
: Maximum attempts before giving best answermax_execution_time
: Timeout in secondsmax_rpm
: Rate limiting for API callsmax_retry_limit
: Retries on error
Code Execution
allow_code_execution
: Must be True to run codecode_execution_mode
:"safe"
: Uses Docker (recommended for production)"unsafe"
: Direct execution (use only in trusted environments)
Advanced Features
multimodal
: Enable multimodal capabilities for processing text and visual contentreasoning
: Enable agent to reflect and create plans before executing tasksinject_date
: Automatically inject current date into task descriptions
Templates
system_template
: Defines agent’s core behaviorprompt_template
: Structures input formatresponse_template
: Formats agent responses
When using custom templates, ensure that both system_template
and prompt_template
are defined. The response_template
is optional but recommended for consistent output formatting.
When using custom templates, you can use variables like {role}
, {goal}
, and {backstory}
in your templates. These will be automatically populated during execution.
Agent Tools
Agents can be equipped with various tools to enhance their capabilities. CrewAI supports tools from:
Here’s how to add tools to an agent:
Agent Memory and Context
Agents can maintain memory of their interactions and use context from previous tasks. This is particularly useful for complex workflows where information needs to be retained across multiple tasks.
When memory
is enabled, the agent will maintain context across multiple interactions, improving its ability to handle complex, multi-step tasks.
Context Window Management
CrewAI includes sophisticated automatic context window management to handle situations where conversations exceed the language model’s token limits. This powerful feature is controlled by the respect_context_window
parameter.
How Context Window Management Works
When an agent’s conversation history grows too large for the LLM’s context window, CrewAI automatically detects this situation and can either:
- Automatically summarize content (when
respect_context_window=True
) - Stop execution with an error (when
respect_context_window=False
)
Automatic Context Handling (respect_context_window=True
)
This is the default and recommended setting for most use cases. When enabled, CrewAI will:
What happens when context limits are exceeded:
- ⚠️ Warning message:
"Context length exceeded. Summarizing content to fit the model context window."
- 🔄 Automatic summarization: CrewAI intelligently summarizes the conversation history
- ✅ Continued execution: Task execution continues seamlessly with the summarized context
- 📝 Preserved information: Key information is retained while reducing token count
Strict Context Limits (respect_context_window=False
)
When you need precise control and prefer execution to stop rather than lose any information:
What happens when context limits are exceeded:
- ❌ Error message:
"Context length exceeded. Consider using smaller text or RAG tools from crewai_tools."
- 🛑 Execution stops: Task execution halts immediately
- 🔧 Manual intervention required: You need to modify your approach
Choosing the Right Setting
Use respect_context_window=True
(Default) when:
- Processing large documents that might exceed context limits
- Long-running conversations where some summarization is acceptable
- Research tasks where general context is more important than exact details
- Prototyping and development where you want robust execution
Use respect_context_window=False
when:
- Precision is critical and information loss is unacceptable
- Legal or medical tasks requiring complete context
- Code review where missing details could introduce bugs
- Financial analysis where accuracy is paramount
Alternative Approaches for Large Data
When dealing with very large datasets, consider these strategies:
1. Use RAG Tools
2. Use Knowledge Sources
Context Window Best Practices
- Monitor Context Usage: Enable
verbose=True
to see context management in action - Design for Efficiency: Structure tasks to minimize context accumulation
- Use Appropriate Models: Choose LLMs with context windows suitable for your tasks
- Test Both Settings: Try both
True
andFalse
to see which works better for your use case - Combine with RAG: Use RAG tools for very large datasets instead of relying solely on context windows
Troubleshooting Context Issues
If you’re getting context limit errors:
If automatic summarization loses important information:
The context window management feature works automatically in the background. You don’t need to call any special functions - just set respect_context_window
to your preferred behavior and CrewAI handles the rest!
Important Considerations and Best Practices
Security and Code Execution
- When using
allow_code_execution
, be cautious with user input and always validate it - Use
code_execution_mode: "safe"
(Docker) in production environments - Consider setting appropriate
max_execution_time
limits to prevent infinite loops
Performance Optimization
- Use
respect_context_window: true
to prevent token limit issues - Set appropriate
max_rpm
to avoid rate limiting - Enable
cache: true
to improve performance for repetitive tasks - Adjust
max_iter
andmax_retry_limit
based on task complexity
Memory and Context Management
- Use
memory: true
for tasks requiring historical context - Leverage
knowledge_sources
for domain-specific information - Configure
embedder
when using custom embedding models - Use custom templates (
system_template
,prompt_template
,response_template
) for fine-grained control over agent behavior
Advanced Features
- Enable
reasoning: true
for agents that need to plan and reflect before executing complex tasks - Set appropriate
max_reasoning_attempts
to control planning iterations (None for unlimited attempts) - Use
inject_date: true
to provide agents with current date awareness for time-sensitive tasks - Customize the date format with
date_format
using standard Python datetime format codes - Enable
multimodal: true
for agents that need to process both text and visual content
Agent Collaboration
- Enable
allow_delegation: true
when agents need to work together - Use
step_callback
to monitor and log agent interactions - Consider using different LLMs for different purposes:
- Main
llm
for complex reasoning function_calling_llm
for efficient tool usage
- Main
Date Awareness and Reasoning
- Use
inject_date: true
to provide agents with current date awareness for time-sensitive tasks - Customize the date format with
date_format
using standard Python datetime format codes - Valid format codes include: %Y (year), %m (month), %d (day), %B (full month name), etc.
- Invalid date formats will be logged as warnings and will not modify the task description
- Enable
reasoning: true
for complex tasks that benefit from upfront planning and reflection
Model Compatibility
- Set
use_system_prompt: false
for older models that don’t support system messages - Ensure your chosen
llm
supports the features you need (like function calling)
Troubleshooting Common Issues
-
Rate Limiting: If you’re hitting API rate limits:
- Implement appropriate
max_rpm
- Use caching for repetitive operations
- Consider batching requests
- Implement appropriate
-
Context Window Errors: If you’re exceeding context limits:
- Enable
respect_context_window
- Use more efficient prompts
- Clear agent memory periodically
- Enable
-
Code Execution Issues: If code execution fails:
- Verify Docker is installed for safe mode
- Check execution permissions
- Review code sandbox settings
-
Memory Issues: If agent responses seem inconsistent:
- Verify memory is enabled
- Check knowledge source configuration
- Review conversation history management
Remember that agents are most effective when configured according to their specific use case. Take time to understand your requirements and adjust these parameters accordingly.